Segmental eigenvoice for rapid speaker adaptation

This paper presents a new approach to improve the conventional eigenvoice technique. In the conventional eigenvoice, an eigenspace is established by introducing a priori training speakers via PCA. The adaptation data is then used to determine a group of coefficients with respect to the eigenspace and build the SD model for the testing speaker. In the proposed approach, the eigenspace in the conventional eigenvoice is segmented into N sub-eigenspaces. Each subeigenspace is established by those components in the training speaker SD models with similar properties to each other. With the adaptation data, N groups of coefficients corresponding to the N sub-eigenspaces can be determined to build SD model for the new testing speaker. Here, both mixture-based and feature-based segmentation of eigenspace were tested, and improved results compared to the conventional eigenvoice were obtained in both cases. Even better results were obtained when these approaches were properly combined.